ValueError: Shapes (2, 35, 1) and () are incompatible

[yang12313]

I got this error when I run the code:

---

ValueError Traceback (most recent call last)
in
271
272 if name == "main":
--> 273 test_RNN()

in test_RNN()
259 model = RNN_Model(config)
260 start_time = time.time()
--> 261 stats = model.train(verbose=True)
262 print('Training time: {}'.format(time.time() - start_time))
263

in train(self, verbose)
216 print('epoch %d'%epoch)
217 if epoch==0:
--> 218 train_acc, val_acc, loss_history, val_loss = self.run_epoch(new_model=True)
219 else:
220 train_acc, val_acc, loss_history, val_loss = self.run_epoch()

in run_epoch(self, new_model, verbose)
176 break
177 tree = self.train_data[step]
--> 178 logits = self.inference(tree)
179 labels = [l for l in tree.labels if l!=2]
180 loss = self.loss(logits, labels)

in inference(self, tree, predict_only_root)
38
39 def inference(self, tree, predict_only_root=False):
---> 40 node_tensors = self.add_model(tree.root)
41 if predict_only_root:
42 node_tensors = node_tensors[tree.root][0]

in add_model(self, node)
81 curr_node_tensor = [curr_node_vec, curr_node_mat]
82 else:
---> 83 node_tensors.update(self.add_model(node.left))
84 node_tensors.update(self.add_model(node.right))
85 tmp = tf.concat(0,

in add_model(self, node)
82 else:
83 node_tensors.update(self.add_model(node.left))
---> 84 node_tensors.update(self.add_model(node.right))
85 tmp = tf.concat(0,
86 [tf.matmul(

in add_model(self, node)
81 curr_node_tensor = [curr_node_vec, curr_node_mat]
82 else:
---> 83 node_tensors.update(self.add_model(node.left))
84 node_tensors.update(self.add_model(node.right))
85 tmp = tf.concat(0,

in add_model(self, node)
82 else:
83 node_tensors.update(self.add_model(node.left))
---> 84 node_tensors.update(self.add_model(node.right))
85 tmp = tf.concat(0,
86 [tf.matmul(

in add_model(self, node)
81 curr_node_tensor = [curr_node_vec, curr_node_mat]
82 else:
---> 83 node_tensors.update(self.add_model(node.left))
84 node_tensors.update(self.add_model(node.right))
85 tmp = tf.concat(0,

in add_model(self, node)
90 tf.matmul(
91 node_tensors[node.left][1],
---> 92 node_tensors[node.right][0]
93 )
94 ])

E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a

E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(values, axis, name)
1252 axis, name="concat_dim",
1253 dtype=dtypes.int32).get_shape().assert_is_compatible_with(
-> 1254 tensor_shape.scalar())
1255 return identity(values[0], name=scope)
1256 return gen_array_ops.concat_v2(values=values, axis=axis, name=name)

E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in assert_is_compatible_with(self, other)
1021 """
1022 if not self.is_compatible_with(other):
-> 1023 raise ValueError("Shapes %s and %s are incompatible" % (self, other))
1024
1025 def most_specific_compatible_shape(self, other):

ValueError: Shapes (2, 35, 1) and () are incompatible

### I ran your script on Jupyter and python 3.
Could you tell me how to fix it?